Artificial intelligence (“AI”) and machine learning (“ML”) capabilities are increasingly sought to improve computer systems to enable them to perform more tasks in practical applications such as driving business efficiency, finding trends in data, and interacting with customers. Traditionally, developing artificial intelligence solutions required a deep data science engineer with specialized skills and knowledge; a relatively small talent pool. This reliance on a small number of people with the requisite skills and experience can result in limited developments of solutions, delays, and greater cost. Further, once such solutions are deployed, they require the continued attention of a data science engineer to maintain, debug, support, administer, and update such solutions. It is therefore desirable to bring simpler systems and methods of developing, deploying, and maintaining solutions to general engineering audiences.